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Retraction 收缩
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-08-28 DOI: 10.1111/coin.70117

RETRACTION: J.D. Kharibam, T. Khelchandra, “ Automatic Speaker Recognition from Speech Signal Using Bidirectional Long Short-term Memory Recurrent Neural Network,” Computational Intelligence 39 no. 2 (2023): 170193, https://doi.org/10.1111/coin.12278.

The above article, published online on 23 January 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

引用本文:J.D. Kharibam, T. Khelchandra,“基于双向长短期记忆递归神经网络的语音信号自动识别”,《计算智能》第39期。2 (2023): 170-193, https://doi.org/10.1111/coin.12278。上述文章于2020年1月23日在线发表在Wiley在线图书馆(wileyonlinelibrary.com)上,经期刊主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者已被告知撤稿。
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引用次数: 0
Retraction 收缩
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-08-28 DOI: 10.1111/coin.70119

RETRACTION: G. Premalatha, P. V. Chandramani, “ Improved Gait Recognition through Gait Energy mage Partitioning,” Computational Intelligence 36 no. 3 (2020): 12611274, https://doi.org/10.1111/coin.12340.

The above article, published online on 22 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

缩回:G. Premalatha, P. V. Chandramani,“基于步态能量图像分割的改进步态识别”,《计算机智能》第36期。3 (2020): 1261-1274, https://doi.org/10.1111/coin.12340。上述文章于2020年6月22日在线发表在Wiley在线图书馆(wileyonlinelibrary.com)上,经主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者已被告知撤稿。
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引用次数: 0
Retraction 收缩
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-08-28 DOI: 10.1111/coin.70121

RETRACTION: S. Narasimhan, M. Arunachalam, “ Bio-PUF-MAC Authenticated Encryption for Iris Biometrics,” Computational Intelligence 36 no. 3 (2020): 1221124, https://doi.org/10.1111/coin.12332.

The above article, published online on 27 May 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.

引用本文:S. Narasimhan, M. **am,“虹膜生物识别技术的生物身份验证加密”,《计算机科学》第36期。3 (2020): 1221-124, https://doi.org/10.1111/coin.12332。上述文章于2020年5月27日在线发表在Wiley在线图书馆(wileyonlinelibrary.com)上,经期刊主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者不同意撤稿。
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引用次数: 0
Enhanced Deep Learning Framework for Precise MRI-Based Alzheimer's Disease Stage Classification 基于mri的阿尔茨海默病分期精确分类的增强深度学习框架
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-08-17 DOI: 10.1111/coin.70123
Saravanan Chandrasekaran, Surbhi Bhatia Khan, Muskan Gupta, T. R. Mahesh, Abdulmajeed Alqhatani, Ahlam Almusharraf

Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify. Traditional approaches are not effective, and new computational techniques are required that can provide fast and accurate diagnosis. In this paper, a novel deep learning methodology that greatly enhances the sensitivity and specificity of AD stage identification by analyzing in-depth MRI scans is proposed. The model applies a novel Sequential Convolutional Neural Network (CNN) architecture, which has been deeply trained on the “Augmented Alzheimer MRI Dataset” made available by Kaggle, to integrate various layers of depth and complexity to identify and scan in-depth features on MRI images. Major enhancements include the use of learning rate schedulers and dropout regularization to fine-tune training as well as avoid overfitting, with a diagnosis accuracy of 94.2%. This level of accuracy not only makes diagnostic processes easier but also allows for early detection of Alzheimer's phases, which is crucial for timely interventions and effective management of the condition. The model is rigorously trained on a large set of augmented data with varying levels of AD to guarantee robustness and generalizability in various demographic and clinical settings. Batch normalization and higher-order activation functions allow faster and stable convergence of training, and thus the model is more efficient and scalable. Application of this model to the clinic has the potential to sharply reduce time to diagnosis, lessen dependence on radiological expertise, and offer a high-accuracy, scalable imaging device enabling early and accurate treatment in Alzheimer's care. This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.

使用MRI扫描诊断阿尔茨海默病(AD)必须非常准确,因为在整个疾病过程中的细微差异很难识别。传统的诊断方法效果不佳,需要新的计算技术来提供快速准确的诊断。本文提出了一种新的深度学习方法,通过分析深度MRI扫描,大大提高了AD分期识别的敏感性和特异性。该模型采用了一种新颖的顺序卷积神经网络(CNN)架构,该架构在Kaggle提供的“增强的阿尔茨海默病MRI数据集”上进行了深度训练,整合了不同层次的深度和复杂性,以识别和扫描MRI图像上的深度特征。主要的改进包括使用学习率调度器和dropout正则化来微调训练以及避免过拟合,诊断准确率为94.2%。这种水平的准确性不仅使诊断过程更容易,而且允许早期发现阿尔茨海默氏症的阶段,这对于及时干预和有效管理病情至关重要。该模型在具有不同程度AD的大量增强数据上进行了严格训练,以保证在各种人口统计学和临床环境中的稳健性和泛化性。批归一化和高阶激活函数使得训练收敛更快、更稳定,从而使模型更高效、可扩展。将该模型应用于临床有可能大幅缩短诊断时间,减少对放射专业知识的依赖,并提供高精度、可扩展的成像设备,使阿尔茨海默病的早期和准确治疗成为可能。这项创新代表了人工智能医学成像的下一个重要阶段,它为阿尔茨海默病的精细检测和分期提供了一种非常有效的工具。
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引用次数: 0
A Machine Learning Approach of Text Classification for High- and Low-Resource Languages 高资源语言和低资源语言文本分类的机器学习方法
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-08-16 DOI: 10.1111/coin.70114
Muhammad Owais Raza, Naeem Ahmed Mahoto, Asadullah Shaikh, Nazia Pathan, Hani Alshahrani, M. A. Elmagzoub

A large amount of data have been published online in textual format for the last decade because of the advancement of information and communication technologies. This is an open challenge to organize and classify large amounts of textual data automatically, especially for a language that has limited resources available online. In this study, two types of approaches are adopted for experiments. First one is a traditional strategy that uses six (06) classical state-of-the-art classification models (1. decision tree (DT), 2. logistic regression (LR), 3. support vector machine (SVM), 4. k-nearest neighbour (k-NN), 5. Naive Bayes (NB), and 6. random forest (RF)) along with two (02) ensemble methods (1. Adaboost and 2. gradient boosting (GB)) and second modeling technique is our proposed voting based ensembling scheme. Models are trained on a 75-25 split where 75% of data is used for training and 25% for testing. The evaluation of the classification models is carried out based on accuracy, precision, recall, and F1-score indexes. The experimental outcomes witnessed that for the traditional approach, gradient boosting outperformed for the limited resource language with 98.08% F1-score, while SVM performed better (97.34% F1-score) for the resource-rich language.

在过去十年中,由于信息和通信技术的进步,大量数据以文本格式在线发布。自动组织和分类大量文本数据是一个公开的挑战,特别是对于在线可用资源有限的语言。在本研究中,实验采用了两种方法。第一种是传统的策略,它使用了6(06)个经典的最先进的分类模型。决策树(DT), 2。2 .逻辑回归(LR);3 .支持向量机;k近邻(k-NN), 5。朴素贝叶斯(NB)和6。随机森林(RF)以及两种(02)集成方法(1)。Adaboost和2。梯度增强(GB)和二次建模技术是我们提出的基于投票的集成方案。模型按照75-25分割进行训练,其中75%的数据用于训练,25%用于测试。根据准确率、精密度、召回率和f1评分指标对分类模型进行评价。实验结果表明,对于传统方法而言,梯度增强在资源有限的语言上表现更好,f1得分为98.08%,而SVM在资源丰富的语言上表现更好(f1得分为97.34%)。
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引用次数: 0
Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning 驯服三角:论机器学习中的公平性、可解释性和隐私之间的相互作用
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-08-06 DOI: 10.1111/coin.70113
Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet, Mohamed Siala

Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution, or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias and do not leak sensitive information regarding their training data. Indeed, interpretability, fairness, and privacy are key requirements for the development of responsible machine learning, and all three have been studied extensively during the last decade. However, they were mainly considered in isolation, while in practice they interplay with each other, either positively or negatively. In this survey paper, we review the literature on the interactions between these three desiderata. More precisely, for each pairwise interaction, we summarize the identified synergies and tensions. These findings highlight several fundamental theoretical and empirical conflicts, while also demonstrating that jointly considering these different requirements is challenging when one aims at preserving a high level of utility. To solve this issue, we also discuss possible conciliation mechanisms, showing that a careful design can enable to successfully handle these different concerns in practice.

机器学习技术越来越多地用于高风险决策,如大学录取、贷款归属或再犯预测。因此,至关重要的是要确保学习的模型可以被人类用户审计或理解,不会产生或再现歧视或偏见,也不会泄露有关其训练数据的敏感信息。事实上,可解释性、公平性和隐私性是负责任机器学习发展的关键要求,在过去十年中,这三者都得到了广泛的研究。然而,它们主要是孤立地考虑的,而在实践中,它们或积极或消极地相互作用。在这篇调查论文中,我们回顾了这三种期望之间相互作用的文献。更准确地说,对于每一个成对的相互作用,我们总结了确定的协同作用和紧张关系。这些发现突出了几个基本的理论和经验冲突,同时也表明,当一个人的目标是保持高水平的效用时,联合考虑这些不同的需求是具有挑战性的。为了解决这个问题,我们还讨论了可能的调解机制,表明仔细的设计可以在实践中成功地处理这些不同的关注点。
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引用次数: 0
Emotion-Based Mental State Classification Using EEG for Brain-Computer Interface Applications 基于情绪的脑电心理状态分类在脑机接口中的应用
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-07-29 DOI: 10.1111/coin.70112
Atta Ur Rahman, Sania Ali, Ritika Wason, Saurabh Aggarwal, Mohammed Abohashrh, Yousef Ibrahim Daradkeh, Inam Ullah

Brain-computer interface (BCI) is a growing area of research in human-computer interaction (HCI), where its potential ranges from medicine to entertainment. It intends to manage various assistive technologies through the utilization of brain signals. This technology acquires and interprets brain signals before sending them to a connected device, which generates controls based on the obtained signals. Emotion-based mental state categorization employing electroencephalogram (EEG) signals is an emerging method of BCI application. However, EEG signals comprise artifacts and redundant or noisy information from the subject, equipment, and external environment. Also, the EEG signals have a low spatial resolution (physical location of the activity within the brain) but a high temporal resolution (millisecond level). Therefore, artifact removal, feature extraction, and classification of EEG signals are challenging. This work proposed a novel approach called Extended Independent Component Analysis (E-ICA) for artifact removal from EEG signals. A Multi-class Common Spatial Pattern (M-CSP) is proposed for feature extraction. A Bidirectional long short-term memory (BiLSTM) network is proposed to improve the classification of EEG signals and fine-tune its parameters. This study leverages the Database for Emotion Analysis using the Physiological Signals (DEAP) dataset to validate the model's performance. This dataset includes EEG recordings annotated with emotional attributes such as valence, arousal, dominance, and liking. After conducting several experiments, the proposed approach achieves a high classification accuracy of 94.61% and outperforms state-of-the-art works. The proposed approach can be successfully integrated into BCI systems for real-time emotion identification in healthcare and user engagement detection in gaming environments.

脑机接口(BCI)是人机交互(HCI)研究的一个新兴领域,其潜力范围从医学到娱乐。它打算通过利用大脑信号来管理各种辅助技术。该技术在将大脑信号发送到连接的设备之前获取并解释大脑信号,该设备根据获得的信号产生控制。基于情绪的脑电信号精神状态分类是一种新兴的脑机接口应用方法。然而,脑电图信号包含来自受试者、设备和外部环境的伪影和冗余或噪声信息。此外,脑电图信号具有低空间分辨率(大脑中活动的物理位置),但具有高时间分辨率(毫秒级)。因此,脑电信号的伪影去除、特征提取和分类是一项具有挑战性的工作。本文提出了一种新的方法,即扩展独立分量分析(E-ICA),用于去除脑电信号中的伪影。提出了一种多类公共空间模式(M-CSP)用于特征提取。提出了一种双向长短期记忆(BiLSTM)网络来改进脑电信号的分类,并对其参数进行微调。本研究利用生理信号(DEAP)数据集的情绪分析数据库来验证模型的性能。该数据集包括带有情感属性注释的脑电图记录,如效价、唤醒、支配和喜欢。经过多次实验,该方法的分类准确率达到了94.61%,优于目前的研究成果。所提出的方法可以成功地集成到BCI系统中,用于医疗保健中的实时情绪识别和游戏环境中的用户参与度检测。
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引用次数: 0
Retraction 收缩
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-07-25 DOI: 10.1111/coin.70111

RETRACTION: H. Rajadurai and U.D. Gandhi, “ An Empirical Model in Intrusion Detection Systems Using Principal Component Analysis and Deep Learning Models,” Computational Intelligence 37 no. 3 (2021): 11111124, https://doi.org/10.1111/coin.12342.

The above article, published online on 05 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

撤回:H. Rajadurai和U.D. Gandhi,“基于主成分分析和深度学习模型的入侵检测系统的经验模型”,《计算机智能》第37期。3 (2021): 1111-1124, https://doi.org/10.1111/coin.12342.The上述文章于2020年6月5日在线发表在Wiley online Library (wileyonlinelibrary.com)上,经主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者已被告知撤稿。
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引用次数: 0
Retraction 收缩
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-07-25 DOI: 10.1111/coin.70110

RETRACTION: A. Rajendran and M. Rajappa, “ Efficient Signal Selection Using Supervised Learning Model for Enhanced State Restoration,” Computational Intelligence 37 no. 3 (2021): 11411154, https://doi.org/10.1111/coin.12344.

The above article, published online on 17 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.

A. Rajendran和M. Rajappa,“基于监督学习模型的高效信号选择”,《计算机智能》第37期。3 (2021): 1141-1154, https://doi.org/10.1111/coin.12344.The上述文章,于2020年6月17日在线发表在Wiley在线图书馆(wileyonlinelibrary.com),经主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者不同意撤稿。
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引用次数: 0
Retraction 收缩
IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-07-25 DOI: 10.1111/coin.70109

RETRACTION: L. Sun, X. Xu, Y. Yang, W. Liu, and J. Jin, “ Knowledge Mapping of Supply Chain Risk Research Based on CiteSpace,” Computational Intelligence 36 no. 4 (2020): 16861703, https://doi.org/10.1111/coin.12306.

The above article, published online on 04 March 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

引用本文:孙丽丽,徐翔,杨勇,刘伟,金俊,“基于CiteSpace的供应链风险知识图谱研究”,《计算机科学》第36期。4 (2020): 1686-1703, https://doi.org/10.1111/coin.12306.The上述文章于2020年3月4日在线发表在Wiley online Library (wileyonlinelibrary.com)上,经主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者已被告知撤稿。
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引用次数: 0
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